AI Services Case Study — Developing an AI Strategy and Roadmap
- Jan 27
- 7 min read
Updated: 3 days ago
1. Organisational Problem
The organisation was a UK-based professional services firm with an established digital environment, a strong client delivery focus and growing interest in how artificial intelligence could improve efficiency, knowledge work and service delivery. Senior leadership recognised the opportunity, but also the need to manage organisational, professional and governance risks carefully.
A prior AI Capability and Maturity Assessment established that the organisation was operating within the Limited Capability bracket, with gaps in governance, organisational understanding and strategic alignment. While individual teams had begun exploring AI opportunities, these activities were not yet connected through a coherent organisation-wide strategy.
Without a clear strategic direction, leadership risked fragmented, inconsistently governed AI activity that was unlikely to deliver sustained value. Leadership therefore faced a critical question:
"How do we move from fragmented AI activity to a structured, organisation-wide approach that delivers measurable value?"
In the Orr Consulting AI Transformation Process, this case study demonstrates the Design-stage role of AI Strategy and Roadmap development in translating discovery evidence into a coherent strategic direction, investment framework and phased path towards delivery.
2. Situation
The board had expressed a clear ambition to use AI in ways that would improve business performance while maintaining appropriate governance, assurance and control. Leadership wanted AI adoption to support operational efficiency, service quality, competitive positioning and responsible innovation.
However, without a defined AI strategy, there was a risk that:
AI initiatives would remain fragmented
investment decisions would become reactive and lack consistency
governance gaps would persist
opportunities to scale AI would be missed
The organisation therefore needed more than a collection of potential AI initiatives. It needed an agreed view of the future state, the capabilities required to reach it, the priorities that should guide investment and the sequence through which adoption could progress safely.
3. Background
Following the Capability and Maturity Assessment, the board set a target of progressing toward the Leading Capability bracket over time.
To support this ambition, the organisation engaged Orr Consulting to develop a structured AI Strategy and Roadmap aligned to organisational objectives and informed by the findings of prior AI Education and Training, AI Capability and Maturity Assessment and AI Use Case Discovery activity.
4. Action Taken
Orr Consulting worked with the board and senior managers to develop a structured AI Strategy and Roadmap while ensuring that the organisation retained ownership of the strategic choices, priorities and delivery decisions.
The engagement brought together evidence from the organisation’s current maturity, strategic objectives, prioritised use cases, capability gaps, governance requirements and expected benefits.
The work followed Orr Consulting’s structured AI Strategy and Roadmap approach.
5. Strategy Outputs
5.1 AI Vision Statement
The AI Vision Statement defined the organisation's desired future state:
The AI vision is to embed AI across our core service delivery, knowledge management and decision-support activities in ways that measurably improve efficiency, quality and scalability. AI will be delivered as a practical enabler of business performance rather than a standalone technology initiative. Its adoption will be shaped by organisational need, strong governance, professional standards and appropriate organisational control.
5.2 Strategic Context
The organisation’s strategic objectives were to improve operational efficiency, enhance client experience, strengthen knowledge management and reuse, enable data-supported decision-making and ensure responsible adoption of AI. AI was positioned as an enabler of these objectives rather than a standalone initiative.
5.3 Current State Assessment
The Capability and Maturity Assessment showed relatively stronger Data Readiness capability (Emerging), but weaker Governance and Assurance capability (Incidental), with most other areas assessed as Limited. This indicated that structured adoption was feasible, but not yet ready to scale safely.
5.4 Future State
5.4.1 Target Capabilities
The target future state included stronger governance and assurance, greater organisation-wide understanding of AI, scalable delivery capability and more integrated use of AI across knowledge management, client engagement and decision-support activities.
5.4.2 Prioritised AI Use Cases
Based on structured AI Use Case Discovery, priority use cases focused on knowledge and service delivery, operational efficiency, client engagement and decision support. Early priorities included AI-assisted drafting, document summarisation, semantic search, meeting summarisation, bid support and executive briefing preparation. These were prioritised based on strategic alignment, practical deliverability, expected value and data readiness. Not all were expected to progress at the same pace, with some suitable for early pilots and others dependent on stronger governance, capability and delivery foundations.
5.5 Capability Gap Analysis
Comparison of the current and target states identified four priority gaps: Governance and Assurance, Education and Training, AI Capability and Strategy and Culture. Without addressing these, the organisation would be unlikely to scale AI safely or realise consistent value. These gaps informed the strategic priorities.
5.6 Strategic Priorities
Three strategic priorities were defined.
Establish AI Governance and Control — Implement an AI governance and assurance framework, supported by acceptable use policies and risk assessment processes.
Build Organisational AI Capability — Strengthen leadership understanding, targeted education and delivery capability for AI-enabled initiatives.
Deliver High-Value AI Use Cases — Prioritise low-complexity, high-value use cases and use early pilots to establish reusable patterns for scaling.
5.7 Indicative Investment Profile
At strategy stage, investment requirements were assessed at a high level based on prioritised use cases, delivery complexity, capability gaps and the scale of governance, integration and organisational change required. The analysis indicated that investment would be driven primarily by capability development, governance, integration and change, rather than by technology licence costs alone. Overall, investment was expected to be moderate and phased, with initial focus on governance and assurance development, leadership capability building and targeted pilot delivery.
This gave leadership an order-of-magnitude view of likely investment requirements, enabling the board to assess the affordability and strategic viability of the roadmap. The strategy could therefore be considered for approval on an informed basis, while more detailed investment decisions would remain subject to subsequent programme and project business cases.
5.8 Risks and Mitigations
High-Level Risk | Mitigation and Roadmap Response |
R1 Governance and assurance risk — AI adoption could outpace governance arrangements, creating exposure in relation to data handling, client confidentiality and regulatory expectations. | Establish an AI governance and assurance framework, including acceptable use policies and oversight arrangements. Reflected in Phase 1 — Foundations. |
R2 Delivery capability risk — The organisation did not yet have an established capability for delivering and scaling AI-enabled initiatives. | Build delivery capability through targeted leadership support, defined delivery approaches and early pilot activity. Reflected across Phase 1 and Phase 2. |
R3 Data readiness risk — Data quality, availability and accessibility varied across prioritised use cases, affecting feasibility and value realisation. | Assess data readiness at use-case level and prioritise early pilots where data requirements were more manageable. Reflected in the sequencing of pilot activity and later scaling decisions. |
5.9 Roadmap and Phasing
The roadmap was structured into three phases.
Phase 1 — Foundations (0–3 months) — Governance framework implementation, acceptable use policy development, leadership education and initiation of selected pilots.
Phase 2 — Pilot and Scale (3–9 months) — Expansion of pilot use cases, knowledge management enhancements and refinement of governance and assurance processes.
Phase 3 — Embed and Optimise (9–18 months) — Scaling of successful use cases, integration into core service delivery and continuous optimisation of capability and governance.
5.10 Benefits and Outcomes
Expected benefits included improved efficiency in knowledge work and service delivery, better access to organisational knowledge, stronger decision support and reduced risk through improved governance and oversight.
The strategy also established an initial basis for defining benefit ownership, measures and review arrangements through a structured AI Benefits Realisation approach as individual initiatives progressed into business case development and delivery.
6. Outcomes
The strategy engagement created several notable outcomes, observations and lessons learned.
6.1 Strategic Direction Agreed
The board gained an agreed organisation-wide direction for AI, connecting business objectives, prioritised use cases, capability development, governance and investment within a single strategic framework.
This reduced the risk of individual initiatives progressing without a coherent organisational purpose.
6.2 Investment Context Clarified
The indicative investment profile gave leadership an order-of-magnitude view of the resources likely to be required and confirmed that capability, governance, integration and organisational change would be more significant cost drivers than technology licences alone.
This enabled the board to assess the affordability and strategic viability of the roadmap before committing to detailed programme or project investment.
6.3 Priorities Established
The roadmap established a phased path from foundational governance and capability development through pilot delivery to wider scaling and optimisation.
This gave the organisation a practical sequence for progressing AI adoption without attempting to address every priority simultaneously.
6.4 Accountability Established
The board approved the strategy and assigned executive accountability to the Chief Operating Officer, supported by the senior leadership team.
This created clear ownership for progressing Phase 1 and maintaining alignment between strategy and subsequent delivery decisions.
6.5 Phase 1 Approved
The board agreed to initiate Phase 1 delivery, focusing initially on governance and assurance, organisational capability and targeted pilot implementation.
This moved the organisation from fragmented interest towards coordinated and controlled transformation activity.
6.6 Strategy Before Scale
The engagement reinforced an important lesson: an AI strategy must do more than express ambition.
Its value comes from connecting current evidence, future direction, investment, governance, capability, use cases, risks, benefits and sequencing within a framework that leaders can approve and act upon.
Without this strategic framework, the organisation risked continuing fragmented activity, inconsistent investment decisions and adoption that outpaced its governance and delivery capability.
7. Final Thoughts
Many organisations recognise the potential of AI but struggle to translate broad ambition into a coherent and actionable strategy.
An AI Strategy and Roadmap provides the structure required to connect organisational objectives, discovery evidence, prioritised use cases, capability development, governance, investment, benefits and delivery sequencing.
In this case, the strategy created a practical bridge between fragmented AI activity and coordinated transformation. It gave the board an agreed future direction, a phased investment path and the governance and capability priorities required to progress responsibly.
The most important outcome was not the strategy document itself. It was the organisation’s ability to make clearer, more consistent and better-informed decisions about what should happen, when it should happen and who should remain accountable.
This AI Services Case Study is part of the Orr Consulting AI Insights Library — structured thinking for AI transformation leaders and decision makers.
If your organisation would benefit from a clearer strategic direction and phased roadmap for AI adoption, we would be pleased to discuss your next AI steps.
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